Prioritization of NIAS for risk assessment

Scientists propose strategy to prioritize non-intentionally added substances detected by non-targeted screening in paperboard FCMs for risk assessment based on exposure estimates and in silico predictions of toxicity

July 6, 2018 Ksenia Groh

In an article published on June 19, 2018, in the peer-reviewed journal Regulatory Toxicology and Pharmacology, scientists propose a strategy to prioritize non-intentionally added substances (NIAS) potentially migrating from food contact materials (FCMs) for further risk assessment.

Eelco Pieke and colleagues from the Technical University of Denmark, National Food Institute, Denmark, worked with NIAS detected by non-targeted mass spectrometry-based screening in the extracts of paperboard FCMs. The methods for preparation of extracts and non-targeted screening, identification, and quantification of NIAS are described in an earlier publication (FPF reported). Here, they propose a method to convert the obtained concentration estimates into tentative exposure assessment and to evaluated the predicted chemical structures by quantitative structure-activity relationships (QSAR) models for carcinogenicity, mutagenicity, and reproductive toxicity. They further describe a decision tree that uses the assessment results to assign the evaluated NIAS into “high priority,” “low priority,” or “unclassified” groups.

Four risk assessors were asked to apply the proposed strategy to the list of 60 chemical compounds selected from those detected in the extracts of two different paperboard FCM samples, a “recycled unused carton pizza box” and a “carton sheet part of the packaging of luxury chocoloates.” The assessors were able to classify compounds as either high or low priority in 60% of the cases, with about half of the compounds in each of the two categories. The high priority substances included “high-concentration compounds, benzophenone derivatives, and dyes,” while the low priority substances included linear alkane amides, oligomers of polyethylene glycol (PEG), and a number of compounds with “simple structures” considered to be of low risk profile. Compounds that remained “unclassified” either had “disagreements between experts (18%) or . . . a perceived lack of data (23%).”

The authors conclude that their strategy “provides valuable information based on tentative data and is able to prioritize discovered chemical compounds for pending risk assessment.” They further note that “the inclusion of more assessors can improve the classification results.” This appears feasible because the prioritization of 60 substances in the present study “was performed in a short time (less than 1 h).”

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